程春田

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:水利工程系

学科:水文学及水资源. 水利水电工程. 电力系统及其自动化. 计算机应用技术

联系方式:ctcheng@dlut.edu.cn

电子邮箱:ctcheng@dlut.edu.cn

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Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

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论文类型:期刊论文

发表时间:2015-10-01

发表刊物:Energies

收录刊物:SCIE、EI、Scopus

卷号:8

期号:10

页面范围:12228-12241

ISSN号:1996-1073

关键字:SHP; power production forecasting; echo state network; Bayesian regularization

摘要:As a novel recurrent neural network (RNN), an echo state network (ESN) that utilizes a reservoir with many randomly connected internal units and only trains the readout, avoids increased complexity of training procedures faced by traditional RNN. The ESN can cope with complex nonlinear systems because of its dynamical properties and has been applied in hydrological forecasting and load forecasting. Due to the linear regression algorithm usually adopted by generic ESN to train the output weights, an ill-conditioned solution might occur, degrading the generalization ability of the ESN. In this study, the ESN with Bayesian regularization (BESN) is proposed for short-term power production forecasting of small hydropower (SHP) plants. According to the Bayesian theory, the weights distribution in space is considered and the optimal output weights are obtained by maximizing the posterior probabilistic distribution. The evidence procedure is employed to gain optimal hyperparameters for the BESN model. The recorded data obtained from the SHP plants in two different counties, located in Yunnan Province, China, are utilized to validate the proposed model. For comparison, the feed-forward neural networks with Levenberg-Marquardt algorithm (LM-FNN) and the generic ESN are also employed. The results indicate that BESN outperforms both LM-FNN and ESN.